Reconstruction of a cone-beam CT image via forward iterative projection matching.
Ontology highlight
ABSTRACT: To demonstrate the feasibility of reconstructing a cone-beam CT (CBCT) image by deformably altering a prior fan-beam CT (FBCT) image such that it matches the anatomy portrayed in the CBCT projection data set.A prior FBCT image of the patient is assumed to be available as a source image. A CBCT projection data set is obtained and used as a target image set. A parametrized deformation model is applied to the source FBCT image, digitally reconstructed radiographs (DRRs) that emulate the CBCT projection image geometry are calculated and compared to the target CBCT projection data, and the deformation model parameters are adjusted iteratively until the DRRs optimally match the CBCT projection data set. The resulting deformed FBCT image is hypothesized to be an accurate representation of the patient's anatomy imaged by the CBCT system. The process is demonstrated via numerical simulation. A known deformation is applied to a prior FBCT image and used to create a synthetic set of CBCT target projections. The iterative projection matching process is then applied to reconstruct the deformation represented in the synthetic target projections; the reconstructed deformation is then compared to the known deformation. The sensitivity of the process to the number of projections and the DRR/CBCT projection mismatch is explored by systematically adding noise to and perturbing the contrast of the target projections relative to the iterated source DRRs and by reducing the number of projections.When there is no noise or contrast mismatch in the CBCT projection images, a set of 64 projections allows the known deformed CT image to be reconstructed to within a nRMS error of 1% and the known deformation to within a nRMS error of 7%. A CT image nRMS error of less than 4% is maintained at noise levels up to 3% of the mean projection intensity, at which the deformation error is 13%. At 1% noise level, the number of projections can be reduced to 8 while maintaining CT image and deformation errors of less than 4% and 13%, respectively. The method is sensitive to contrast mismatch between the simulated projections and the target projections when the soft-tissue contrast in the projections is low.By using prior knowledge available in a FBCT image, the authors show that a CBCT image can be iteratively reconstructed from a comparatively small number of projection images, thus saving acquisition time and reducing imaging dose. This will enable more frequent daily imaging during radiation therapy. Because the process preserves the CT numbers of the FBCT image, the resulting 3D image intensities will be more accurate than a CBCT image reconstructed via conventional backprojection methods. Reconstruction errors are insensitive to noise at levels beyond what would typically be found in CBCT projection data, but are sensitive to contrast mismatch errors between the CBCT projection data and the DRRs.
Project description:To generalize and experimentally validate a novel algorithm for reconstructing the 3D pose (position and orientation) of implanted brachytherapy seeds from a set of a few measured 2D cone-beam CT (CBCT) x-ray projections.The iterative forward projection matching (IFPM) algorithm was generalized to reconstruct the 3D pose, as well as the centroid, of brachytherapy seeds from three to ten measured 2D projections. The gIFPM algorithm finds the set of seed poses that minimizes the sum-of-squared-difference of the pixel-by-pixel intensities between computed and measured autosegmented radiographic projections of the implant. Numerical simulations of clinically realistic brachytherapy seed configurations were performed to demonstrate the proof of principle. An in-house machined brachytherapy phantom, which supports precise specification of seed position and orientation at known values for simulated implant geometries, was used to experimentally validate this algorithm. The phantom was scanned on an ACUITY CBCT digital simulator over a full 660 sinogram projections. Three to ten x-ray images were selected from the full set of CBCT sinogram projections and postprocessed to create binary seed-only images.In the numerical simulations, seed reconstruction position and orientation errors were approximately 0.6 mm and 5 degrees, respectively. The physical phantom measurements demonstrated an absolute positional accuracy of (0.78 +/- 0.57) mm or less. The theta and phi angle errors were found to be (5.7 +/- 4.9) degrees and (6.0 +/- 4.1) degrees, respectively, or less when using three projections; with six projections, results were slightly better. The mean registration error was better than 1 mm/6 degrees compared to the measured seed projections. Each test trial converged in 10-20 iterations with computation time of 12-18 min/iteration on a 1 GHz processor.This work describes a novel, accurate, and completely automatic method for reconstructing seed orientations, as well as centroids, from a small number of radiographic projections, in support of intraoperative planning and adaptive replanning. Unlike standard back-projection methods, gIFPM avoids the need to match corresponding seed images on the projections. This algorithm also successfully reconstructs overlapping clustered and highly migrated seeds in the implant. The accuracy of better than 1 mm and 6 degrees demonstrates that gIFPM has the potential to support 2D Task Group 43 calculations in clinical practice.
Project description:Dedicated cone-beam breast computed tomography (CBBCT) is an emerging modality and provides fully three-dimensional (3D) images of the uncompressed breast at an isotropic voxel resolution. In an effort to translate this modality to breast cancer screening, advanced image reconstruction methods are being pursued. Since radiographic breast density is an established risk factor for breast cancer and CBBCT provides volumetric data, this study investigates the reproducibility of the volumetric glandular fraction (VGF), defined as the proportion of fibroglandular tissue volume relative to the total breast volume excluding the skin. Four image reconstruction methods were investigated: the analytical Feldkamp-Davis-Kress (FDK), a compressed sensing-based fast, regularized, iterative statistical technique (FRIST), a fully supervised deep learning approach using a multi-scale residual dense network (MS-RDN), and a self-supervised approach based on Noise-to-Noise (N2N) learning. Projection datasets from 106 women who participated in a prior clinical trial were reconstructed using each of these algorithms at a fixed isotropic voxel size of (0.273 mm3). Each reconstructed breast volume was segmented into skin, adipose, and fibroglandular tissues, and the VGF was computed. The VGF did not differ among the four reconstruction methods (p = 0.167), and none of the three advanced image reconstruction algorithms differed from the standard FDK reconstruction (p > 0.862). Advanced reconstruction algorithms developed for low-dose CBBCT reproduce the VGF to provide quantitative breast density, which can be used for risk estimation.
Project description:Objective.A dedicated cone-beam breast computed tomography (BCT) using a high-resolution, low-noise detector operating in offset-detector geometry has been developed. This study investigates the effects of varying detector offsets and image reconstruction algorithms to determine the appropriate combination of detector offset and reconstruction algorithm.Approach.Projection datasets (300 projections in 360°) of 30 breasts containing calcified lesions that were acquired using a prototype cone-beam BCT system comprising a 40 × 30 cm flat-panel detector with 1024 × 768 detector pixels were reconstructed using Feldkamp-Davis-Kress (FDK) algorithm and served as the reference. The projection datasets were retrospectively truncated to emulate cone-beam datasets with sinograms of 768×768 and 640×768 detector pixels, corresponding to 5 cm and 7.5 cm lateral offsets, respectively. These datasets were reconstructed using the FDK algorithm with appropriate weights and an ASD-POCS-based Fast, total variation-Regularized, Iterative, Statistical reconstruction Technique (FRIST), resulting in a total of 4 offset-detector reconstructions (2 detector offsets × 2 reconstruction methods). Signal difference-to-noise ratio (SDNR), variance, and full-width at half-maximum (FWHM) of calcifications in two orthogonal directions were determined from all reconstructions. All quantitative measurements were performed on images in units of linear attenuation coefficient (1/cm).Results.The FWHM of calcifications did not differ (P > 0.262) among reconstruction algorithms and detector formats, implying comparable spatial resolution. For a chosen detector offset, the FRIST algorithm outperformed FDK in terms of variance and SDNR (P < 0.0001). For a given reconstruction method, the 5 cm offset provided better results.Significance.This study indicates the feasibility of using the compressed sensing-based, FRIST algorithm to reconstruct sinograms from offset-detectors. Among the reconstruction methods and detector offsets studied, FRIST reconstructions corresponding to a 30 cm × 30 cm with 5 cm lateral offset, achieved the best performance. A clinical prototype using such an offset geometry has been developed and installed for clinical trials.
Project description:OBJECTIVES::To determine the optimized kV setting for a narrow detector cone-beam CT (CBCT) unit. METHODS::Clinical (CL) and quantitative (QUANT) evaluations of image quality were performed using an anthropomorphic phantom. Technical (TECH) evaluation was performed with a polymethyl methacrylate phantom. Images were obtained using a PaX-i3D Green CBCT (Vatech, Hwaseong, Korea) device, with a large 21 × 19?and a medium 12 × 9?cm field of view (FOV), and high-dose (HD-ranging from 85 to 110 kV) and low-dose (LD-ranging from 75 to 95 kV) protocols, totaling four groups (21 × 19?cm HD, 21 × 19?cm LD, 12 × 9?cm HD, 12 × 9?cm LD). The radiation dose within each group was fixed by adapting the mA according to a predetermined dose-area product. For CL evaluation, three observers assessed images based on overall quality, sharpness, contrast, artefacts, and noise. For QUANT evaluation, mean gray value shift, % increase of standard deviation (SD), % of beam hardening and contrast-to-noise ratio (CNR) were calculated. For TECH evaluation, segmentation accuracy, CNR, metal artefact SD, metal object area, and sharpness were measured. Representative parameters were chosen for CL, QUANT, and TECH evaluations to determine the optimal kV based on biplot graphs. kV values of the same protocol were compared by the bootstrapping approach. The ones that had statistical differences with the best kV were considered as worse quality. RESULTS::Overall, kV values within the same group showed similar quality (p > 0.05), except for 110 kV in 21 × 19?cm HD and 85 kV in 12 × 9?cm HD of CL score; also 85, 90 kV in 21 × 19?cm HD and 75, 80 kV in 21 × 19?cm LD of QUANT score which were worse (p < 0.05). CONCLUSION::At a constant dose, low and high kV protocols yield acceptable image quality for a narrow-detector CBCT unit.
Project description:BackgroundTo develop a low-dose cone beam CT (LD-CBCT) reconstruction method named simultaneous algebraic reconstruction technique and dual-dictionary learning (SART-DDL) joint algorithm for image guided radiation therapy (IGRT) and evaluate its imaging quality and clinical application ability.MethodsIn this retrospective study, 62 CBCT image sets from February 2018 to July 2018 at west china hospital were randomly collected from 42 head and neck patients (mean [standard deviation] age, 49.7 [11.4] years, 12 females and 30 males). All image sets were retrospectively reconstructed by SART-DDL (resultant D-CBCT image sets) with 18% less clinical raw projections. Reconstruction quality was evaluated by quantitative parameters compared with SART and Total Variation minimization (SART-TV) joint reconstruction algorithm with paired t test. Five-grade subjective grading evaluations were done by two oncologists in a blind manner compared with clinically used Feldkamp-Davis-Kress algorithm CBCT images (resultant F-CBCT image sets) and the grading results were compared by paired Wilcoxon rank test. Registration results between D-CBCT and F-CBCT were compared. D-CBCT image geometry fidelity was tested.ResultsThe mean peak signal to noise ratio of D-CBCT was 1.7 dB higher than SART-TV reconstructions (P < .001, SART-DDL vs SART-TV, 36.36 ± 0.55 dB vs 34.68 ± 0.28 dB). All D-CBCT images were recognized as clinically acceptable without significant difference with F-CBCT in subjective grading (P > .05). In clinical registration, the maximum translational and rotational difference was 1.8 mm and 1.7 degree respectively. The horizontal, vertical and sagittal geometry fidelity of D-CBCT were acceptable.ConclusionsThe image quality, geometry fidelity and clinical application ability of D-CBCT are comparable to that of the F-CBCT for head-and-neck patients with 18% less projections by SART-DDL.
Project description:In computer vision, reference datasets from simulation and real outdoor scenes have been highly successful in promoting algorithmic development in stereo reconstruction. Endoscopic stereo reconstruction for surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surface properties and the presence of blood and smoke. These issues present difficulties for both stereo reconstruction itself and also for standardised dataset production. Previous datasets have been produced using computed tomography (CT) or structured light reconstruction on phantom or ex vivo models. We present a stereo-endoscopic reconstruction validation dataset based on cone-beam CT (SERV-CT). Two ex vivo small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Subsequent orientation of the endoscope was manually aligned to match the stereoscopic view and benchmark disparities, depths and occlusions are calculated. The requirement of a CT scan limited the number of stereo pairs to 8 from each ex vivo sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of around 2 pixels and a depth accuracy of about 2 mm. A simplified reference dataset is provided consisting of endoscope image pairs with corresponding calibration, disparities, depths and occlusions covering the majority of the endoscopic image and a range of tissue types, including smooth specular surfaces, as well as significant variation of depth. We assessed the performance of various stereo algorithms from online available repositories. There is a significant variation between algorithms, highlighting some of the challenges of surgical endoscopic images. The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths covering the majority of the endoscopic image. This complements existing resources well and we hope will aid the development of surgical endoscopic anatomical reconstruction algorithms.
Project description:PurposeTo compare the image quality between a deep learning-based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT.MethodsHead CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa.ResultsDLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader.ConclusionThe image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.
Project description:PurposeTo compare cone-beam computed tomography (CT) navigation vs conventional CT image guidance during biopsies.Materials and methodsPatients scheduled for image-guided biopsies were prospectively and randomly assigned to conventional CT guidance vs cone-beam CT navigation. Radiation dose, accuracy of final needle position, rate of histopathologic diagnosis, and number of needle repositions to reach the target (defined as pullback to adjust position) were compared.ResultsA total of 58 patients (mean age, 57 y; 62.1% men) were randomized: 29 patients underwent 33 biopsies with CT guidance and 29 patients with 33 lesions underwent biopsy with cone-beam CT navigation. The average body mass index (BMI) was similar between groups, at 28.8 kg/m(2) ± 6.55 (P = .18). There was no difference between groups in terms of patient and lesion characteristics (eg, size, depth). The average lesion size was 29.1 ± 12.7mm for CT group vs 32.1mm ±16.8mm for cone-beam CT group (P < 0.59). Location of lesions was equally divided between the 2 groups, 20 lung lesions, 18 renal lesions and 20 other abdominal lesions. Mean number of needle repositions in the cone-beam CT group was 0.3 ± 0.5, compared with 1.9 ± 2.3 with conventional CT (P < .001). The average skin entry dose was 29% lower with cone-beam CT than with conventional CT (P < .04 accounting for BMI). The average estimated effective dose for the planning scan from phantom data was 49% lower with cone-beam CT vs conventional CT (P = .018). Accuracy, defined as the difference between planned and final needle positions, was 4.9 mm ± 4.1 for the cone-beam CT group, compared with 12.2 mm ± 8.1 for conventional CT (P < .001). Histopathologic diagnosis rates were similar between groups, at 90.9% for conventional CT and 93.9% for cone-beam CT (P = .67).ConclusionsCone-beam CT navigation for biopsies improved targeting accuracy with fewer needle repositions, lower skin entry dose, and lower effective dose for planning scan, and a comparable histopathologic diagnosis rate.
Project description:To investigate tight-frame based iterative reconstruction (TFIR) technique for spectral breast computed tomography (CT) using fewer projections while achieving greater image quality.The experimental data were acquired with a fan-beam breast CT system based on a cadmium zinc telluride photon-counting detector. The images were reconstructed with a varying number of projections using the TFIR and filtered backprojection (FBP) techniques. The image quality between these two techniques was evaluated. The image's spatial resolution was evaluated using a high-resolution phantom, and the contrast to noise ratio (CNR) was evaluated using a postmortem breast sample. The postmortem breast samples were decomposed into water, lipid, and protein contents based on images reconstructed from TFIR with 204 projections and FBP with 614 projections. The volumetric fractions of water, lipid, and protein from the image-based measurements in both TFIR and FBP were compared to the chemical analysis.The spatial resolution and CNR were comparable for the images reconstructed by TFIR with 204 projections and FBP with 614 projections. Both reconstruction techniques provided accurate quantification of water, lipid, and protein composition of the breast tissue when compared with data from the reference standard chemical analysis.Accurate breast tissue decomposition can be done with three fold fewer projection images by the TFIR technique without any reduction in image spatial resolution and CNR. This can result in a two-third reduction of the patient dose in a multislit and multislice spiral CT system in addition to the reduced scanning time in this system.
Project description:In this study, we investigate the feasibility of improving the imaging quality for low-dose multislice helical computed tomography (CT) via iterative reconstruction with tensor framelet (TF) regularization. TF based algorithm is a high-order generalization of isotropic total variation regularization. It is implemented on a GPU platform for a fast parallel algorithm of X-ray forward band backward projections, with the flying focal spot into account. The solution algorithm for image reconstruction is based on the alternating direction method of multipliers or the so-called split Bregman method. The proposed method is validated using the experimental data from a Siemens SOMATOM Definition 64-slice helical CT scanner, in comparison with FDK, the Katsevich and the total variation (TV) algorithm. To test the algorithm performance with low-dose data, ACR and Rando phantoms were scanned with different dosages and the data was equally undersampled with various factors. The proposed method is robust for the low-dose data with 25% undersampling factor. Quantitative metrics have demonstrated that the proposed algorithm achieves superior results over other existing methods.